Obesity affects a significant adult population worldwide and is associated with many human diseases such as autoimmune diseases, cardiovascular disorders, type 2 diabetes, respiratory diseases, and cancers. However, obesity is less studied than other human diseases like cancer or blood disorder. The global prevalence and fast-growing pace of obesity reflects critical and complex causal factors in modern society. Some epidemiological studies have shown that dietary intake such as fat and sugar is highly associated with obesity. Thus, understanding the mechanisms of diet-induced obesity will provide fundamental knowledge to prevent or treat obesity clinically or in daily life. Interestingly, the human microbiome contributes vital functions to human health or disease traits. Emerging evidence shows that the gut microbiome is intrinsically associated with obesity risk. Microbes can also be used as a revolutionary medical treatment, i.e. through fecal microbiota transplantation. Understanding the metabolic role of gut microbes in diet-induced obesity may provide alternative strategies to prevent or treat obesity. Previous studies have revealed a common core of bacteria comprised of phyla Firmicutes, Bacteroidetes and Actinobacteria, while the rest of the population can be diverse. The insights of this complex and unculturable microbe community and the critical functionalities are still very preliminary, as is understanding of host-microbe interaction in the gut. Despite a collection of culturable microbes identified in the human gut, a systematic and cost-effective way to co-culture the arbitrary microbe community with host cells or tissues is lacking. Thus, the solution relies heavily on the computation algorithm to fill the gap between host and microbes in understanding diet-induced obesity. In this project, three specific aims are proposed to apply scalable computation methods to understand the role of gut microbe community, host intestine cells, and host-microbe interactions that contribute to diet-induced obesity. These aims are to: (1) identify a broader range of gut microbiota (culturable and unculturable) associated with obesity; (2) characterize heterogeneous host cell functions by single-cell transcriptomics; and (3) resolve host-microbe interactions by metabolomic modeling and genetic marks. The datasets - including multi-modal meta-omics gut microbial data and intestinal single-cell transcriptomics - will be generated in-house on mouse models with high-fat, medium-fat, and control diet plans. Successful completion of this project will provide deeper biological insights of the metabolic roles of gut microbes, host cells and host-microbe interactions associated with diet induced obesity, and also a powerful set of large-scale computational tools and techniques to conduct the host-microbe research in obesity or other related phenotypes.